Nowadays several medical image acquisition techniques and systems exist that render a digital signal representation of a medical image, e.g. a radiographic image.
One example of such a system is a computed radiography system wherein a radiation image is recorded on a temporary storage medium, more particularly a photostimulable phosphor screen. In such a system a digital signal representation is obtained by scanning the screen with radiation of (a) wavelength(s) within the stimulating wavelength range of the phosphor and by detecting the light emitted by the phosphor upon stimulation.
Other examples of computed radiography systems are direct radiography systems, for example systems wherein a radiographic image is recorded in a solid state sensor comprising a radiation sensitive layer and a layer of electronic read out circuitry.
Still another example of a computed radiography system is a system wherein a radiographic image is recorded on a conventional x-ray film and wherein that film is developed and subsequently subjected to image scanning.
Still other systems such as a tomography system may be envisaged.
The digital image representation of the medical image acquired by one of the above systems can then be used for generating a visible image on which the diagnosis can be performed. For this purpose the digital signal representation is applied to a hard copy recorder or to a display device.
Commonly the digital signal representation of the image is subjected to image processing prior to hard copy recording or display.
In order to convert the digital image information optimally into a visible image on a medium on which the diagnosis is performed, a multi-resolution image processing method has been developed by means of which the contrast of an image is enhanced.
According to this multi-resolution image processing method an image represented by an array of pixel values is processed by applying the following steps. First the original image is decomposed into a sequence of detail images at multiple scales and a residual image.
Next, the pixel values of the detail images are modified by applying to these pixel values at least one non-linear monotonically increasing odd conversion function with a slope that gradually decreases with increasing argument values. In a specific embodiment the slope of the conversion function is gradually decreasing with the exception that it may be constant or increasing in a lower subrange which is assumed to represent mostly noise.
Finally, a processed image is computed by applying a reconstruction algorithm to the residual image and the modified detail images, the reconstruction algorithm being such that if it were applied to the residual image and the detail images without modification, then the original image or a close approximation thereof would be obtained.
The above image processing technique has been described extensively in European patent EP 527 525, the processing being referred to as MUSICA image processing (MUSICA is a registered trade name of Agfa-Gevaert N.V.).
The described method is advantageous over conventional image processing techniques such as unsharp masking etc. because it increases the visibility of subtle details in the image and because it increases the faithfulness of the image reproduction without introducing artefacts.
In practice the contrast enhancement processing is adapted to different types of images, e.g. to different examination types.
The state of the art workflow is commonly as follows.
A digital representation of a radiation image acquired by an image acquisition system as described higher is applied to an image-processing module together with a code indicative of the examination type. In the image processing module parameter settings for different examination types and examination sub-types, identifiable by means of the code, are stored in advance for example in the form of a look up table. Upon entry of a code, the parameters corresponding with the examination type identified by the code are retrieved from the look up table and the image processing including the contrast enhancement processing is controlled by the retrieved parameter settings.
Determining the optimal parameter values for each examination type is highly time-consuming because before a set of processing parameters can be accepted for a certain examination type, this set of parameters has to be evaluated on a large number of test images of that specific examination type.
The number of individual examination types is rather large since for each anatomic region (e.g. thorax) a number of examination subtypes exist (bed, paediatric, etc.).
Hence the total number of parameters to be determined is large.
In most-large hospitals a centralised, radiology specific information system (RIS) is available so that the information on the examination type can be sent via this RIS.
If such a system is not available, the information on the examination type must be entered manually into an identification system. This kind of identification is more error prone than the RIS system.
In the former years the use of computed radiography systems was to a large extent limited to large radiography centres in the western market. Nowadays more smaller hospitals and private practices started to use digital systems. In addition also in non-industrialised regions computed radiography is gaining importance.
The adjustment and control of the image processing parameters demands for extensive knowledge of medical applications, which knowledge is often not available in or too expensive for these smaller and/or less industrialised centres.
The fact that in prior art methods the processing parameters had to be determined in advance and had to be fed to the processing apparatus thus constitutes a drawback of prior art methods.